Differential recurrent neural network based predictive control

Computers and Chemical Engineering

Volumn 32, Issue 7, 2008, Pages 1533-1545

  Al Seyab, R K ^a   Cao, Yi ^a  

^a CRANFIELD UNIVERSITY   (United Kingdom)

Author keywords

Automatic differentiation.; Nonlinear systems; Predictive control; Recurrent neural network; System identification

Indexed keywords

ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); NONLINEAR SYSTEMS; PROBLEM SOLVING; RECURRENT NEURAL NETWORKS;

AUTOMATIC DIFFERENTIATION; OPTIMIZATION PROBLEM;

PREDICTIVE CONTROL SYSTEMS;

ALGORITHMS; IDENTIFICATION (CONTROL SYSTEMS); NONLINEAR SYSTEMS; PREDICTIVE CONTROL SYSTEMS; PROBLEM SOLVING; RECURRENT NEURAL NETWORKS;

EID: 42949171800     PISSN: 00981354     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.compchemeng.2007.07.007     Document Type: Article

References (36)

1. Billings S.A., and Voon W.S.F. Correlation based model validity tests for nonlinear models. International Journal of Control 44 (1986) 235-244
2. Cao, Y. (1995). Control structure selection for chemical process using input-output controllability analysis. PhD Thesis, University of Exeter.
3. Cao Y. A formulation of nonlinear model predictive control using automatic differentiation. Journal of Process Control 15 8 (2005) 851-858
4. Cao Y., and Al-Seyab R. Nonlinear model predictive control using automatic differentiation. in CDROM. European control conference (ECC 2003). Cambridge, UK (2003)
5. Cao Y., and Biss D. An extension of singular value analysis for assessing manipulated variable constraints. Journal of Process Control 6 1 (1996) 37-48
6. Cao Y., and Yang Z.J. Muiltobjective process controllability analysis. Computers and Chemical Engineering 28 1-2 (2004) 83-90
7. Christianson B. Reverse accumulation and accurate rounding error estimates for Taylor series. Optimization Methods and Software 1 (1992) 81-94
8. Chu J.Z., Jang S.S., and Chen Y.N. A comparative study of combined feedforward/feedback model predictive control for nonlinear systems. Canadian Journal of Chemical Engineering 82 6 (2004) 1263-1272
9. Delgado A., Kambhampati C., and Warwick K. Dynamic recurrent neural network for system identification and control. IEE Proceedings-Control and Applications 142 4 (1995) 307-314
10. Fabro J.A., Arruda L.V.R., and Neves Jr. F. Startup of a distillation column using intelligent control techniques. Computers and Chemical Engineering 30 (2005) 309-320
11. Funahashi K.L., and Nakamura Y. Approximation of dynamical systems by continues time recurrent neural networks. Neural Networks 6 (1993) 183-192
12. Goldberge D.E. Genetic algorithms in search, optimization and machine learning (1989), Addision-Wesley, Reading, MA
13. Griesse R., and Walther A. Evaluating gradients in optimal control: Continuous adjoint versus automatic differentiation. Journal of Optimization Theory and Applications 122 1 (2004) 63-86
14. Griewank A., David J., and Jean U. ADOL-C: A package for the automatic differentiation of algorithms written in C/C++. ACM Transactions on Mathematical Software 22 (1996) 131-167
15. Henson M.A. Nonlinear model predictive control: Current states and future directions. Computer and Chemical Engineering 23 (1998) 187-202
16. Hush D.R., and Horne B.G. Progress in supervised neural networks. IEEE Signal Process, Magazine 1 (1993) 8-39
17. Jazayeri-Rad H. The nonlinear model predictive control of a chemical plant using multiple neural networks. Neural Computer and Application 13 (2004) 2-15
18. Jin L., Nikiforuk P., and Gupta M. Approximation of discrete-time state-space trajectories using dynamic recurrent neural networks. IEEE Transactions on Automatic Control 40 7 (1995) 1266-1270
19. Kambhampati C., Craddock R.J., Tham M., and Warwick K. Inverse model control using recurrent networks. Mathematics and Computers in Simulation 51 (2000) 181-199
20. Kambhampati C., Garces F., and Warwick K. Approximation of non-autonomous dynamic systems by continuous time recurrent neural networks. Proceeding of the IEEE-INNS-ENNS International Joint Conference on Neural Networks IJCNN, 1 (2000) 64-69
21. Korenberg M.J., and Paarmann L.D. Orthogonal approaches to time-series analysis and system identification. IEEE Signal Process, Magazine 7 (1991) 29-43
22. Leonard J.A., and Kramer M.A. Improvement of the back-propagation algorithm for training neural networks. Chemical Engineering 14 (1999) 337-341
23. Marquardt D. An algorithm for least-squares estimation of nonlinear parameters. SIAM Journal of Applied Mathematics 11 (1963) 431-441
24. Miller W.T., Sutton R.S., and Werbos P.J. Neural networks for control (1990), MIT Press, Cambridge, MA
25. Narendra K.S., and Parthasarathy K. Identification and control of dynamical systems using neural networks. IEEE Transactions on Neural Networks 1 1 (1990) 4-26
26. Norgaard M., Ravn O., and Poulsen N.K. Neural networks for modeling and control of dynamic systems-A practitioners handbook (2000), Springer-Verlag
27. Pearlmutter B.A. Gradient calculations for dynamic recurrent neural networks: A survey. IEEE Transactions on Neural Networks. (1995)
28. Rumelhart D.E., Hinton G.E., and Williams R.J. Learning internal representations by error propagation. Parallel Distributed Processing (1986), MIT Press, Cambridge MA
29. Schlegel M., Marquardt W., Ehrig R., and Nowak U. Sensitivity analysis of linearly implicit differential-algebraic systems by one-step extrapolation. Applied Numerical Mathematics 48 (2004) 83-102
30. Storen S., and Hertzberg T. Obtaining sensitivity information in dynamic optimization problems solved by the sequential approach. Computer and Chemical Engineering 23 (1999) 807-819
31. Su, H. T., & McAvoy, T. J. (1997). Artificial neural networks for nonlinear process identification and control, In: Henson, M. A. & Seborg, D.E. (Eds). Nonlinear Process Control, pp. 371-428, Englewood Cliffs, NJ: Prentice-Hall.
32. Tan Y., and Cauwenberghe A. Nonlinear one step ahead control using neural networks: Control strategy and stability design. Automatica 32 12 (1996) 1701-1706
33. Temeng H., Schenelle P., and McAvoy T. Model predictive control of an industrial packed reactors using neural networks. Journal of Process Control 5 1 (1995) 19-28
34. Zamarreno J.M., and Vega P. State-space neural network, properties and application. Neural Networks 11 (1998) 1099-1112
35. Zhang J., and Morris J. Recurrent neuro-fuzzy networks for nonlinear process modeling. IEEE Transactions on Neural Networks 10 2 (1999) 313-326
36. Zhao H., Guiver J., and Sentoni G. An identification approach to nonlinear state space model for industrial multivariable model predictive control. Proceeding of the American Control Conference. Philadelphia, Pennsylvania (1998)